Set the targets with your own numbers, not ours. Price an hour of unplanned downtime on your highest-throughput line, pull last year's scrap and rework cost as a share of COGS by customer program, and ask what preventing even a third of it is worth. Those are the levers this system pulls: fewer shift-level production stoppages, fewer parts scrapped per work order, lower scrap PPM and rework rates - and OEE improving as yield loss becomes predictable and preventable rather than reactive. We model the specific targets against your plant's production data during scoping, before you commit.
ROI compounds over 12 months because the system's accuracy improves as it learns your operation's specific yield signatures. In months 1-3, you capture the quick wins - obvious parameter drifts and material-condition combinations that were already visible to experienced operators but not systematized. Months 4-9, the model detects subtle multi-factor interactions (a material lot from Supplier A + humidity above 65% + machine calibration drift = 35% scrap on this SKU) that no individual shift supervisor would have connected. By month 12, the target state is yield loss that is largely predictable: a plant that has shifted from crisis-driven quality work to proactive line tuning, with shift supervisors spending their time on continuous improvement rather than firefighting. Supply chain and procurement teams use yield predictions to negotiate tighter material specs and supplier SLAs, creating structural cost reductions that persist beyond the AI deployment.